A novel approach for building occupancy simulation

Wang, Chuang; Yan, Da; Jiang, Yi

doi:10.1007/s12273-011-0044-5

Cited by 210 publications

(128 citation statements)

References 10 publications

(13 reference statements)

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“…individual tracking, as listed in Table 3. Wang et al [24] proposes an approach for building occupancy simulation based on the Markov chain. By using the Markov chain method to simulate this stochastic movement process, the model can generate the location for each occupant and further aggregated as the zone-level occupancy for the whole building.…”

Section: Occupancy Model Level 4: Space Location Of An Occupantmentioning

confidence: 99%

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Simulation of occupancy in buildings

Feng

Yan

Hong

2015

Energy and Buildings

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192

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Occupants are involved in a variety of activities in buildings, which drive them to move among rooms, enter or leave a building. In this study, occupancy is defined at four levels and varies with time: (1) the number of occupants in a building, (2) occupancy status of a space, (3) the number of occupants in a space, and (4) the space location of an occupant. Occupancy has a great influence on internal loads and ventilation requirement, thus building energy consumption. Based on a comprehensive review and comparison of literature on occupancy modeling, three representative occupancy models, corresponding to the levels 2, 3, and 4, are selected and implemented in a software module. Main contributions of our study include: (1) new methods to classify occupancy models, (2) the review and selection of various levels of occupancy models, and (3) new methods to integrate these model into a tool that can be used in different ways for different applications and by different audiences. The software can simulate more detailed occupancy in buildings to improve the simulation of energy use, and better evaluate building technologies in buildings. The occupancy of an office building is simulated as an example to demonstrate the use of the software module.

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Section: Occupancy Model Level 4: Space Location Of An Occupantmentioning

confidence: 99%

“…Wang's model [24] is selected to generate the space location of each occupant and the space-level occupancy for the whole building. The core concept is the use of Markov chain.…”

Section: Occupancy Model Level 4: Space Location Of An Occupantmentioning

confidence: 99%

Simulation of occupancy in buildings

Feng

Yan

Hong

2015

Energy and Buildings

Self Cite

192

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“…time of arrival and departure, periods of intermediate absence and presence, as well as periods of long absence from the space) based on and validated by sensor data, and were later used as an input to occupant behavior models within building simulation tools [13]. C. Wang et al [14] handled occupancy as the straightforward result of occupant movement processes which occurred among the spaces inside and outside a building. By using the Markov Chain method, the model generated the location for each occupant and the zone-level occupancy for a whole office building type.…”

Section: Introductionmentioning

confidence: 99%

Occupancy schedules learning process through a data mining framework

D’Oca

Hong

2015

Energy and Buildings

240

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Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10 minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. The identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.

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“…future residential condition, future ambient climate status, etc. ), which are stochastic in nature (Mc Duling 2006;Hussain, Ansari 2010;Wang et al 2011b;.…”

Section: Introductionmentioning

confidence: 99%

“…hourly) varies but can be predicted based on historical data (Wang et al 2011b), nevertheless, the long term behaviour (e.g. yearly or for even longer period) is relatively steady (i.e.…”

Section: Introductionmentioning

confidence: 99%

Lifecycle Energy Consumption Prediction of Residential Buildings by Incorporating Longitudinal Uncertainties

Wang

Shen

2014

Journal of Civil Engineering and Management

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Abstract. Accurate prediction of buildings' lifecycle energy consumption is a critical part in lifecycle assessment of residential buildings. Longitudinal variations in building conditions, weather conditions and building's service life can cause significant deviation of the prediction from the real lifecycle energy consumption. The objective is to improve the accuracy of lifecycle energy consumption prediction by properly modelling the longitudinal variations in residential energy consumption model using Markov chain based stochastic approach. A stochastic Markov model considering longitudinal uncertainties in building condition, degree days, and service life is developed: 1) Building's service life is estimated through Markov deterioration curve derived from actual building condition data; 2) Neural Network is used to project periodic energy consumption distribution for each joint energy state of building condition and temperature state; 3) Lifecycle energy consumption is aggregated based on Markov process and the state probability. A case study on predicting lifecycle energy consumption of a residential building is presented using the proposed model and the result is compared to that of a traditional deterministic model and three years' measured annual energy consumptions. It shows that the former model generates much narrower distribution than the latter model when compared to the measured data, which indicates improved result.

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A novel approach for building occupancy simulation

Cited by 210 publications

References 10 publications

Simulation of occupancy in buildings

Simulation of occupancy in buildings

Occupancy schedules learning process through a data mining framework

Lifecycle Energy Consumption Prediction of Residential Buildings by Incorporating Longitudinal Uncertainties

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